An Intelligent Hybrid System for Fracture Detection and Healing Time Estimation from X- ray Images Using Lead-Lag CompensatorAnalyser

The traditional monitoring of bone fracture healing relies on multiple X-ray images, which can expose patients to unnecessary radiation. To address this, this work introduces a new method based on a transfer function derived from X-ray images to estimate bone healing time, reducing the need for repeated imaging. Data collected from Thanjavur Medical College Hospital initially identifies whether a fracture is present. The healing time is then determined using a specific approach. The method focuses on tibia fractures and justifies using a single original X-ray image to extract key fracture-related features such as phase lag, fracture area, fracture perimeter, and centroid coordinates (X, Y). These features train three machine learning models—Coarse Tree, Ensemble Bagged Trees, and Linear SVM—to classify fractures, achieving accuracies of 90%, 70%, and 80%, respectively. The centroid data used to estimate healing time are converted into a transfer function that describes the fracture condition. The phase lag derived from this function acts as the main indicator of bone healing progress. To improve prediction accuracy, a lead–lag compensator is designed and implemented to reduce the phase lag, effectively making the system respond faster. Incorporating control-theoretic principles into medical imaging is a novel aspect of this work, enabling the estimation of healing time without the need for serial radiographs. Real-time experiments on tibia fractures show that the estimated healing time closely matches clinician-recorded recovery periods. The method proves accurate across different fracture types when predicting healing times. Besides its precision, this new technique offers benefits such as reduced radiation exposure, cost savings, and clinical practicality.